federated learning

Supplementary Material for "Characterizing the Maintaining Effect of Backdoors in Federated Learning"

Sequential Backdoor Retention Experiments This repository studies whether an implanted backdoor remains detectable after subsequent training. Each experiment compares continued benign training with training that injects a second backdoor. Evaluation measures retention of the first backdoor using both current and restor...

FedLCD: A Federated Learning Framework with Layer-Wise Consistency Detection

Federated learning enables multiple clients to collaboratively train a global model without sharing raw data, but it is vulnerable to Byzantine attacks, in which malicious clients upload manipulated model updates to disrupt training or degrade model accuracy. Existing Byzantine-robust aggregation methods typically oper...

Zero-Trust Federated Learning with Differential Privacy for Cross-Domain Point-of-Sale Recommendation Networks

A Multi-Layer Security Architecture for Smart-City IoT with Measured Limits: Edge Intrusion Detection, Screened Federated Learning and Ledger-Anchored Model Provenance

Smart-city platforms connect IoT devices across transport, energy, water, healthcare and e-government, so every gateway is an entry point. This paper evaluates a four-layer architecture—edge intrusion detection trained across districts by federated learning, digests anchored on a permissioned ledger, and a cloud orches...

Privacy in India’s digital credit ecosystem: a systematic literature review and policy directions

This study examines India’s digital credit ecosystem, exploring the relationship between technological efficiency and user rights, particularly data privacy. It aims to identify emerging trends, key concerns, and potential regulatory and technological interventions. The PRISMA framework guided a systematic review of sc...

Cyber-Resilient Digital Twin Architectures for Smart Grids: A Review of Attack Modeling, Detection, Mitigation, and Secure Control Strategies

The evidence indicates that a qualifying cyber-resilient digital twin is distinguished not merely by the presence of a virtual model, but by bidirectional physical–virtual synchronization combined with explicit cyber-resilience functions.

FAR-Q: a Fog-Assisted fedeRated Q-Learning protocol for stable, energy-aware and adaptive routing in flying Ad Hoc networks

Machine Learning and Deep Learning for Medical Diagnosis: A Modality-Wise Review of Clinical Records, Biosignals and Medical Images

Machine learning now supports diagnosis in many clinical settings, from risk scores built on patient records to deep networks that read scans. The evidence, however, is split by data type. Studies on tabular records, biosignals and medical images use different models, different metrics and different validation practice...

Privacy-preserving techniques in visual computing: applications, challenges, and future directions

The proliferation of imaging technologies across healthcare, agriculture, environments, and consumer applications has heightened concerns regarding the privacy and security of visual data, which may contain biometric, medical, spatial, and location-specific information. Although federated learning (FL), differential pr...

Trustworthy Federated Intrusion Detection for the Future Internet: A Critical Review and Evaluation Framework

Federated intrusion detection for the Future Internet must operate across heterogeneous devices, distributed telemetry, changing traffic patterns, and adversarial clients. This critical structured review synthesizes a targeted corpus of 37 publications and standards, including 20 Future Internet articles, coded on six...

Integrating cure and care in Alzheimer’s disease: a systems perspective for precision medicine

Alzheimer’s disease remains a major global health challenge, with limited disease-modifying therapies and a rapidly growing societal burden. Although amyloid and tau remain central to the field, current evidence also implicates neuroinflammation, immune dysregulation, vascular dysfunction, and host-microbe interactions...

Understanding Federated Learning Through the Lens of Mechanism Design: The Role of Data Heterogeneity